Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations234
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory82.6 KiB
Average record size in memory361.7 B

Variable types

Numeric13
Text3
Categorical1

Alerts

1970 Population is highly overall correlated with 1980 Population and 9 other fieldsHigh correlation
1980 Population is highly overall correlated with 1970 Population and 9 other fieldsHigh correlation
1990 Population is highly overall correlated with 1970 Population and 9 other fieldsHigh correlation
2000 Population is highly overall correlated with 1970 Population and 9 other fieldsHigh correlation
2010 Population is highly overall correlated with 1970 Population and 9 other fieldsHigh correlation
2015 Population is highly overall correlated with 1970 Population and 9 other fieldsHigh correlation
2020 Population is highly overall correlated with 1970 Population and 9 other fieldsHigh correlation
2022 Population is highly overall correlated with 1970 Population and 9 other fieldsHigh correlation
Area (km²) is highly overall correlated with 1970 Population and 10 other fieldsHigh correlation
Density (per km²) is highly overall correlated with Area (km²)High correlation
Rank is highly overall correlated with 1970 Population and 9 other fieldsHigh correlation
World Population Percentage is highly overall correlated with 1970 Population and 9 other fieldsHigh correlation
Rank is uniformly distributed Uniform
Rank has unique values Unique
CCA3 has unique values Unique
Country/Territory has unique values Unique
Capital has unique values Unique
2022 Population has unique values Unique
2020 Population has unique values Unique
2015 Population has unique values Unique
2010 Population has unique values Unique
2000 Population has unique values Unique
1990 Population has unique values Unique
1980 Population has unique values Unique
1970 Population has unique values Unique
Density (per km²) has unique values Unique
World Population Percentage has 57 (24.4%) zeros Zeros

Reproduction

Analysis started2025-04-20 19:02:47.374738
Analysis finished2025-04-20 19:03:08.603093
Duration21.23 seconds
Software versionydata-profiling vv4.14.0
Download configurationconfig.json

Variables

Rank
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct234
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.5
Minimum1
Maximum234
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-04-20T16:03:08.718748image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12.65
Q159.25
median117.5
Q3175.75
95-th percentile222.35
Maximum234
Range233
Interquartile range (IQR)116.5

Descriptive statistics

Standard deviation67.694165
Coefficient of variation (CV)0.57612055
Kurtosis-1.2
Mean117.5
Median Absolute Deviation (MAD)58.5
Skewness0
Sum27495
Variance4582.5
MonotonicityNot monotonic
2025-04-20T16:03:08.897904image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 1
 
0.4%
93 1
 
0.4%
54 1
 
0.4%
6 1
 
0.4%
232 1
 
0.4%
56 1
 
0.4%
150 1
 
0.4%
210 1
 
0.4%
120 1
 
0.4%
127 1
 
0.4%
Other values (224) 224
95.7%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
234 1
0.4%
233 1
0.4%
232 1
0.4%
231 1
0.4%
230 1
0.4%
229 1
0.4%
228 1
0.4%
227 1
0.4%
226 1
0.4%
225 1
0.4%

CCA3
Text

Unique 

Distinct234
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size13.8 KiB
2025-04-20T16:03:09.280602image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique234 ?
Unique (%)100.0%

Sample

1st rowAFG
2nd rowALB
3rd rowDZA
4th rowASM
5th rowAND
ValueCountFrequency (%)
afg 1
 
0.4%
aut 1
 
0.4%
vgb 1
 
0.4%
bra 1
 
0.4%
dza 1
 
0.4%
asm 1
 
0.4%
and 1
 
0.4%
ago 1
 
0.4%
aia 1
 
0.4%
atg 1
 
0.4%
Other values (224) 224
95.7%
2025-04-20T16:03:09.794843image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 51
 
7.3%
R 51
 
7.3%
M 51
 
7.3%
N 46
 
6.6%
S 40
 
5.7%
L 40
 
5.7%
G 37
 
5.3%
T 36
 
5.1%
B 36
 
5.1%
E 30
 
4.3%
Other values (16) 284
40.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 702
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 51
 
7.3%
R 51
 
7.3%
M 51
 
7.3%
N 46
 
6.6%
S 40
 
5.7%
L 40
 
5.7%
G 37
 
5.3%
T 36
 
5.1%
B 36
 
5.1%
E 30
 
4.3%
Other values (16) 284
40.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 702
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 51
 
7.3%
R 51
 
7.3%
M 51
 
7.3%
N 46
 
6.6%
S 40
 
5.7%
L 40
 
5.7%
G 37
 
5.3%
T 36
 
5.1%
B 36
 
5.1%
E 30
 
4.3%
Other values (16) 284
40.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 702
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 51
 
7.3%
R 51
 
7.3%
M 51
 
7.3%
N 46
 
6.6%
S 40
 
5.7%
L 40
 
5.7%
G 37
 
5.3%
T 36
 
5.1%
B 36
 
5.1%
E 30
 
4.3%
Other values (16) 284
40.5%

Country/Territory
Text

Unique 

Distinct234
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size15.2 KiB
2025-04-20T16:03:10.097971image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length32
Median length24
Mean length9.0512821
Min length4

Characters and Unicode

Total characters2118
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique234 ?
Unique (%)100.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAmerican Samoa
5th rowAndorra
ValueCountFrequency (%)
islands 10
 
3.2%
and 9
 
2.9%
saint 6
 
1.9%
republic 4
 
1.3%
united 4
 
1.3%
new 3
 
1.0%
guinea 3
 
1.0%
south 3
 
1.0%
sudan 2
 
0.6%
the 2
 
0.6%
Other values (261) 269
85.4%
2025-04-20T16:03:10.576366image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 315
14.9%
n 178
 
8.4%
i 176
 
8.3%
e 147
 
6.9%
r 121
 
5.7%
o 108
 
5.1%
t 88
 
4.2%
u 84
 
4.0%
81
 
3.8%
s 80
 
3.8%
Other values (43) 740
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2118
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 315
14.9%
n 178
 
8.4%
i 176
 
8.3%
e 147
 
6.9%
r 121
 
5.7%
o 108
 
5.1%
t 88
 
4.2%
u 84
 
4.0%
81
 
3.8%
s 80
 
3.8%
Other values (43) 740
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2118
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 315
14.9%
n 178
 
8.4%
i 176
 
8.3%
e 147
 
6.9%
r 121
 
5.7%
o 108
 
5.1%
t 88
 
4.2%
u 84
 
4.0%
81
 
3.8%
s 80
 
3.8%
Other values (43) 740
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2118
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 315
14.9%
n 178
 
8.4%
i 176
 
8.3%
e 147
 
6.9%
r 121
 
5.7%
o 108
 
5.1%
t 88
 
4.2%
u 84
 
4.0%
81
 
3.8%
s 80
 
3.8%
Other values (43) 740
34.9%

Capital
Text

Unique 

Distinct234
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
2025-04-20T16:03:10.871584image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length19
Median length14
Mean length7.9273504
Min length4

Characters and Unicode

Total characters1855
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique234 ?
Unique (%)100.0%

Sample

1st rowKabul
2nd rowTirana
3rd rowAlgiers
4th rowPago Pago
5th rowAndorra la Vella
ValueCountFrequency (%)
city 5
 
1.8%
san 4
 
1.5%
saint 4
 
1.5%
port 3
 
1.1%
town 3
 
1.1%
pago 2
 
0.7%
kabul 1
 
0.4%
the 1
 
0.4%
john’s 1
 
0.4%
valley 1
 
0.4%
Other values (250) 250
90.9%
2025-04-20T16:03:11.339032image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 264
 
14.2%
o 131
 
7.1%
i 128
 
6.9%
n 123
 
6.6%
e 113
 
6.1%
r 105
 
5.7%
u 86
 
4.6%
t 79
 
4.3%
s 72
 
3.9%
l 69
 
3.7%
Other values (54) 685
36.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1855
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 264
 
14.2%
o 131
 
7.1%
i 128
 
6.9%
n 123
 
6.6%
e 113
 
6.1%
r 105
 
5.7%
u 86
 
4.6%
t 79
 
4.3%
s 72
 
3.9%
l 69
 
3.7%
Other values (54) 685
36.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1855
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 264
 
14.2%
o 131
 
7.1%
i 128
 
6.9%
n 123
 
6.6%
e 113
 
6.1%
r 105
 
5.7%
u 86
 
4.6%
t 79
 
4.3%
s 72
 
3.9%
l 69
 
3.7%
Other values (54) 685
36.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1855
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 264
 
14.2%
o 131
 
7.1%
i 128
 
6.9%
n 123
 
6.6%
e 113
 
6.1%
r 105
 
5.7%
u 86
 
4.6%
t 79
 
4.3%
s 72
 
3.9%
l 69
 
3.7%
Other values (54) 685
36.9%

Continent
Categorical

Distinct6
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Africa
57 
Asia
50 
Europe
50 
North America
40 
Oceania
23 

Length

Max length13
Median length7
Mean length7.2863248
Min length4

Characters and Unicode

Total characters1705
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAsia
2nd rowEurope
3rd rowAfrica
4th rowOceania
5th rowEurope

Common Values

ValueCountFrequency (%)
Africa 57
24.4%
Asia 50
21.4%
Europe 50
21.4%
North America 40
17.1%
Oceania 23
9.8%
South America 14
 
6.0%

Length

2025-04-20T16:03:11.505159image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T16:03:11.657336image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
africa 57
19.8%
america 54
18.8%
asia 50
17.4%
europe 50
17.4%
north 40
13.9%
oceania 23
8.0%
south 14
 
4.9%

Most occurring characters

ValueCountFrequency (%)
a 207
12.1%
r 201
11.8%
i 184
10.8%
A 161
 
9.4%
c 134
 
7.9%
e 127
 
7.4%
o 104
 
6.1%
u 64
 
3.8%
f 57
 
3.3%
t 54
 
3.2%
Other values (10) 412
24.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1705
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 207
12.1%
r 201
11.8%
i 184
10.8%
A 161
 
9.4%
c 134
 
7.9%
e 127
 
7.4%
o 104
 
6.1%
u 64
 
3.8%
f 57
 
3.3%
t 54
 
3.2%
Other values (10) 412
24.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1705
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 207
12.1%
r 201
11.8%
i 184
10.8%
A 161
 
9.4%
c 134
 
7.9%
e 127
 
7.4%
o 104
 
6.1%
u 64
 
3.8%
f 57
 
3.3%
t 54
 
3.2%
Other values (10) 412
24.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1705
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 207
12.1%
r 201
11.8%
i 184
10.8%
A 161
 
9.4%
c 134
 
7.9%
e 127
 
7.4%
o 104
 
6.1%
u 64
 
3.8%
f 57
 
3.3%
t 54
 
3.2%
Other values (10) 412
24.2%

2022 Population
Real number (ℝ)

High correlation  Unique 

Distinct234
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34074415
Minimum510
Maximum1.4258873 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-04-20T16:03:11.825414image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum510
5-th percentile17689.6
Q1419738.5
median5559944.5
Q322476505
95-th percentile1.1829633 × 108
Maximum1.4258873 × 109
Range1.4258868 × 109
Interquartile range (IQR)22056766

Descriptive statistics

Standard deviation1.3676642 × 108
Coefficient of variation (CV)4.0137571
Kurtosis90.464741
Mean34074415
Median Absolute Deviation (MAD)5477773
Skewness9.1512048
Sum7.973413 × 109
Variance1.8705055 × 1016
MonotonicityNot monotonic
2025-04-20T16:03:12.011620image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41128771 1
 
0.4%
10142619 1
 
0.4%
26207977 1
 
0.4%
218541212 1
 
0.4%
1934 1
 
0.4%
26069416 1
 
0.4%
2093599 1
 
0.4%
49551 1
 
0.4%
5434319 1
 
0.4%
4576298 1
 
0.4%
Other values (224) 224
95.7%
ValueCountFrequency (%)
510 1
0.4%
1871 1
0.4%
1934 1
0.4%
3780 1
0.4%
4390 1
0.4%
5862 1
0.4%
10967 1
0.4%
11312 1
0.4%
11572 1
0.4%
12668 1
0.4%
ValueCountFrequency (%)
1425887337 1
0.4%
1417173173 1
0.4%
338289857 1
0.4%
275501339 1
0.4%
235824862 1
0.4%
218541212 1
0.4%
215313498 1
0.4%
171186372 1
0.4%
144713314 1
0.4%
127504125 1
0.4%

2020 Population
Real number (ℝ)

High correlation  Unique 

Distinct234
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33501071
Minimum520
Maximum1.4249298 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-04-20T16:03:12.190853image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile17641.95
Q1415284.5
median5493074.5
Q321447980
95-th percentile1.1394095 × 108
Maximum1.4249298 × 109
Range1.4249293 × 109
Interquartile range (IQR)21032695

Descriptive statistics

Standard deviation1.3558988 × 108
Coefficient of variation (CV)4.0473296
Kurtosis90.959773
Mean33501071
Median Absolute Deviation (MAD)5412201.5
Skewness9.1835011
Sum7.8392506 × 109
Variance1.8384615 × 1016
MonotonicityNot monotonic
2025-04-20T16:03:12.377907image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38972230 1
 
0.4%
9749640 1
 
0.4%
24333639 1
 
0.4%
208327405 1
 
0.4%
1942 1
 
0.4%
25867467 1
 
0.4%
2111072 1
 
0.4%
49587 1
 
0.4%
5379839 1
 
0.4%
4543399 1
 
0.4%
Other values (224) 224
95.7%
ValueCountFrequency (%)
520 1
0.4%
1827 1
0.4%
1942 1
0.4%
3747 1
0.4%
4500 1
0.4%
5906 1
0.4%
10681 1
0.4%
11069 1
0.4%
11655 1
0.4%
12315 1
0.4%
ValueCountFrequency (%)
1424929781 1
0.4%
1396387127 1
0.4%
335942003 1
0.4%
271857970 1
0.4%
227196741 1
0.4%
213196304 1
0.4%
208327405 1
0.4%
167420951 1
0.4%
145617329 1
0.4%
125998302 1
0.4%

2015 Population
Real number (ℝ)

High correlation  Unique 

Distinct234
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31729956
Minimum564
Maximum1.3937154 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-04-20T16:03:12.554894image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum564
5-th percentile17759.35
Q1404676
median5307400
Q319730854
95-th percentile1.0266771 × 108
Maximum1.3937154 × 109
Range1.3937149 × 109
Interquartile range (IQR)19326178

Descriptive statistics

Standard deviation1.3040499 × 108
Coefficient of variation (CV)4.1098384
Kurtosis91.883996
Mean31729956
Median Absolute Deviation (MAD)5229730.5
Skewness9.2393933
Sum7.4248098 × 109
Variance1.7005462 × 1016
MonotonicityNot monotonic
2025-04-20T16:03:12.741214image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33753499 1
 
0.4%
8682174 1
 
0.4%
20128124 1
 
0.4%
183995785 1
 
0.4%
1847 1
 
0.4%
25258015 1
 
0.4%
2107962 1
 
0.4%
51514 1
 
0.4%
5190356 1
 
0.4%
4191776 1
 
0.4%
Other values (224) 224
95.7%
ValueCountFrequency (%)
564 1
0.4%
1454 1
0.4%
1847 1
0.4%
3408 1
0.4%
5059 1
0.4%
5978 1
0.4%
9643 1
0.4%
10877 1
0.4%
11185 1
0.4%
12182 1
0.4%
ValueCountFrequency (%)
1393715448 1
0.4%
1322866505 1
0.4%
324607776 1
0.4%
259091970 1
0.4%
210969298 1
0.4%
205188205 1
0.4%
183995785 1
0.4%
157830000 1
0.4%
144668389 1
0.4%
127250933 1
0.4%

2010 Population
Real number (ℝ)

High correlation  Unique 

Distinct234
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29845235
Minimum596
Maximum1.3481914 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-04-20T16:03:12.928737image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum596
5-th percentile18075.2
Q1393149
median4942770.5
Q319159568
95-th percentile91127409
Maximum1.3481914 × 109
Range1.3481908 × 109
Interquartile range (IQR)18766418

Descriptive statistics

Standard deviation1.2421849 × 108
Coefficient of variation (CV)4.1620878
Kurtosis92.783332
Mean29845235
Median Absolute Deviation (MAD)4865097
Skewness9.2878279
Sum6.983785 × 109
Variance1.5430233 × 1016
MonotonicityNot monotonic
2025-04-20T16:03:13.127811image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28189672 1
 
0.4%
7583269 1
 
0.4%
16647543 1
 
0.4%
160952853 1
 
0.4%
1812 1
 
0.4%
24686435 1
 
0.4%
2093828 1
 
0.4%
54087 1
 
0.4%
4889741 1
 
0.4%
2881914 1
 
0.4%
Other values (224) 224
95.7%
ValueCountFrequency (%)
596 1
0.4%
1367 1
0.4%
1812 1
0.4%
3187 1
0.4%
4938 1
0.4%
6052 1
0.4%
8988 1
0.4%
10241 1
0.4%
10550 1
0.4%
13142 1
0.4%
ValueCountFrequency (%)
1348191368 1
0.4%
1240613620 1
0.4%
311182845 1
0.4%
244016173 1
0.4%
196353492 1
0.4%
194454498 1
0.4%
160952853 1
0.4%
148391139 1
0.4%
143242599 1
0.4%
128105431 1
0.4%

2000 Population
Real number (ℝ)

High correlation  Unique 

Distinct234
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26269469
Minimum651
Maximum1.2640991 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-04-20T16:03:13.313219image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum651
5-th percentile17747.55
Q1327242
median4292907
Q315762301
95-th percentile79893829
Maximum1.2640991 × 109
Range1.2640984 × 109
Interquartile range (IQR)15435059

Descriptive statistics

Standard deviation1.1169821 × 108
Coefficient of variation (CV)4.2520162
Kurtosis95.835248
Mean26269469
Median Absolute Deviation (MAD)4215096
Skewness9.4204564
Sum6.1470557 × 109
Variance1.2476489 × 1016
MonotonicityNot monotonic
2025-04-20T16:03:13.705174image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19542982 1
 
0.4%
5508297 1
 
0.4%
11622665 1
 
0.4%
122851984 1
 
0.4%
2074 1
 
0.4%
23367059 1
 
0.4%
2037936 1
 
0.4%
80338 1
 
0.4%
4491202 1
 
0.4%
2344253 1
 
0.4%
Other values (224) 224
95.7%
ValueCountFrequency (%)
651 1
0.4%
1666 1
0.4%
2074 1
0.4%
3080 1
0.4%
5138 1
0.4%
6274 1
0.4%
7082 1
0.4%
9638 1
0.4%
10377 1
0.4%
11047 1
0.4%
ValueCountFrequency (%)
1264099069 1
0.4%
1059633675 1
0.4%
282398554 1
0.4%
214072421 1
0.4%
175873720 1
0.4%
154369924 1
0.4%
146844839 1
0.4%
129193327 1
0.4%
126803861 1
0.4%
122851984 1
0.4%

1990 Population
Real number (ℝ)

High correlation  Unique 

Distinct234
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22710221
Minimum700
Maximum1.1537043 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-04-20T16:03:13.886816image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum700
5-th percentile14649.35
Q1264115.75
median3825409.5
Q311869231
95-th percentile71272767
Maximum1.1537043 × 109
Range1.1537036 × 109
Interquartile range (IQR)11605115

Descriptive statistics

Standard deviation97832173
Coefficient of variation (CV)4.3078477
Kurtosis100.6432
Mean22710221
Median Absolute Deviation (MAD)3756236.5
Skewness9.6053732
Sum5.3141917 × 109
Variance9.5711341 × 1015
MonotonicityNot monotonic
2025-04-20T16:03:14.075972image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10694796 1
 
0.4%
3864972 1
 
0.4%
8370647 1
 
0.4%
95214257 1
 
0.4%
2533 1
 
0.4%
20799523 1
 
0.4%
2044174 1
 
0.4%
48002 1
 
0.4%
4241636 1
 
0.4%
1804524 1
 
0.4%
Other values (224) 224
95.7%
ValueCountFrequency (%)
700 1
0.4%
1669 1
0.4%
2332 1
0.4%
2533 1
0.4%
5168 1
0.4%
6324 1
0.4%
8316 1
0.4%
9182 1
0.4%
9598 1
0.4%
10805 1
0.4%
ValueCountFrequency (%)
1153704252 1
0.4%
870452165 1
0.4%
248083732 1
0.4%
182159874 1
0.4%
150706446 1
0.4%
148005704 1
0.4%
123686321 1
0.4%
115414069 1
0.4%
107147651 1
0.4%
95214257 1
0.4%

1980 Population
Real number (ℝ)

High correlation  Unique 

Distinct234
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18984617
Minimum733
Maximum9.8237247 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-04-20T16:03:14.263030image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum733
5-th percentile11242.9
Q1229614.25
median3141145.5
Q39826053.8
95-th percentile60310978
Maximum9.8237247 × 108
Range9.8237173 × 108
Interquartile range (IQR)9596439.5

Descriptive statistics

Standard deviation81785186
Coefficient of variation (CV)4.3079714
Kurtosis102.77609
Mean18984617
Median Absolute Deviation (MAD)3076690.5
Skewness9.657636
Sum4.4424004 × 109
Variance6.6888167 × 1015
MonotonicityNot monotonic
2025-04-20T16:03:14.450058image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12486631 1
 
0.4%
3104788 1
 
0.4%
6173177 1
 
0.4%
72951439 1
 
0.4%
3637 1
 
0.4%
17973650 1
 
0.4%
1907023 1
 
0.4%
17613 1
 
0.4%
4085776 1
 
0.4%
1017462 1
 
0.4%
Other values (224) 224
95.7%
ValueCountFrequency (%)
733 1
0.4%
1647 1
0.4%
2240 1
0.4%
2983 1
0.4%
3637 1
0.4%
6106 1
0.4%
6560 1
0.4%
7598 1
0.4%
7635 1
0.4%
7731 1
0.4%
ValueCountFrequency (%)
982372466 1
0.4%
696828385 1
0.4%
223140018 1
0.4%
148177096 1
0.4%
138257420 1
0.4%
122288383 1
0.4%
117624196 1
0.4%
83929765 1
0.4%
80624057 1
0.4%
77786703 1
0.4%

1970 Population
Real number (ℝ)

High correlation  Unique 

Distinct234
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15786909
Minimum752
Maximum8.2253445 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-04-20T16:03:14.630234image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum752
5-th percentile8427.1
Q1155997
median2604830
Q38817329
95-th percentile54109866
Maximum8.2253445 × 108
Range8.225337 × 108
Interquartile range (IQR)8661332

Descriptive statistics

Standard deviation67795092
Coefficient of variation (CV)4.2943867
Kurtosis103.54767
Mean15786909
Median Absolute Deviation (MAD)2546617
Skewness9.6464663
Sum3.6941367 × 109
Variance4.5961745 × 1015
MonotonicityNot monotonic
2025-04-20T16:03:14.807431image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10752971 1
 
0.4%
2489059 1
 
0.4%
4669708 1
 
0.4%
55569264 1
 
0.4%
5185 1
 
0.4%
14996879 1
 
0.4%
1656783 1
 
0.4%
10143 1
 
0.4%
3875546 1
 
0.4%
670693 1
 
0.4%
Other values (224) 224
95.7%
ValueCountFrequency (%)
752 1
0.4%
1714 1
0.4%
2274 1
0.4%
2417 1
0.4%
5185 1
0.4%
5537 1
0.4%
5665 1
0.4%
5802 1
0.4%
5814 1
0.4%
6260 1
0.4%
ValueCountFrequency (%)
822534450 1
0.4%
557501301 1
0.4%
200328340 1
0.4%
130093010 1
0.4%
115228394 1
0.4%
105416839 1
0.4%
96369875 1
0.4%
78294583 1
0.4%
67541860 1
0.4%
59290872 1
0.4%

Area (km²)
Real number (ℝ)

High correlation 

Distinct233
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean581449.38
Minimum1
Maximum17098242
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-04-20T16:03:14.979758image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile72.05
Q12650
median81199.5
Q3430425.75
95-th percentile2155428.6
Maximum17098242
Range17098241
Interquartile range (IQR)427775.75

Descriptive statistics

Standard deviation1761840.9
Coefficient of variation (CV)3.0300847
Kurtosis43.500864
Mean581449.38
Median Absolute Deviation (MAD)80948.5
Skewness6.082019
Sum1.3605916 × 108
Variance3.1040832 × 1012
MonotonicityNot monotonic
2025-04-20T16:03:15.170134image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 2
 
0.9%
652230 1
 
0.4%
462840 1
 
0.4%
923768 1
 
0.4%
260 1
 
0.4%
120538 1
 
0.4%
25713 1
 
0.4%
464 1
 
0.4%
323802 1
 
0.4%
309500 1
 
0.4%
Other values (223) 223
95.3%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
6 1
0.4%
12 1
0.4%
21 2
0.9%
26 1
0.4%
30 1
0.4%
34 1
0.4%
53 1
0.4%
54 1
0.4%
ValueCountFrequency (%)
17098242 1
0.4%
9984670 1
0.4%
9706961 1
0.4%
9372610 1
0.4%
8515767 1
0.4%
7692024 1
0.4%
3287590 1
0.4%
2780400 1
0.4%
2724900 1
0.4%
2381741 1
0.4%

Density (per km²)
Real number (ℝ)

High correlation  Unique 

Distinct234
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean452.12704
Minimum0.0261
Maximum23172.267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-04-20T16:03:15.353332image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.0261
5-th percentile4.292705
Q138.417875
median95.34675
Q3238.93325
95-th percentile900.99705
Maximum23172.267
Range23172.241
Interquartile range (IQR)200.51537

Descriptive statistics

Standard deviation2066.1219
Coefficient of variation (CV)4.5697817
Kurtosis87.033034
Mean452.12704
Median Absolute Deviation (MAD)72.676
Skewness8.9489416
Sum105797.73
Variance4268859.7
MonotonicityNot monotonic
2025-04-20T16:03:15.527529image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63.0587 1
 
0.4%
21.9139 1
 
0.4%
20.6851 1
 
0.4%
236.5759 1
 
0.4%
7.4385 1
 
0.4%
216.2755 1
 
0.4%
81.4218 1
 
0.4%
106.7909 1
 
0.4%
16.7828 1
 
0.4%
14.7861 1
 
0.4%
Other values (224) 224
95.7%
ValueCountFrequency (%)
0.0261 1
0.4%
0.3105 1
0.4%
2.1654 1
0.4%
2.1727 1
0.4%
3.1092 1
0.4%
3.4032 1
0.4%
3.6204 1
0.4%
3.6459 1
0.4%
3.7621 1
0.4%
3.7727 1
0.4%
ValueCountFrequency (%)
23172.2667 1
0.4%
18234.5 1
0.4%
8416.4634 1
0.4%
6783.3922 1
0.4%
5441.5 1
0.4%
1924.4876 1
0.4%
1745.9567 1
0.4%
1687.6139 1
0.4%
1299.2647 1
0.4%
1188.5926 1
0.4%

GrowthRate
Real number (ℝ)

Distinct180
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0095774
Minimum0.912
Maximum1.0691
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-04-20T16:03:15.699752image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.912
5-th percentile0.99403
Q11.001775
median1.0079
Q31.01695
95-th percentile1.03014
Maximum1.0691
Range0.1571
Interquartile range (IQR)0.015175

Descriptive statistics

Standard deviation0.013384985
Coefficient of variation (CV)0.013258008
Kurtosis12.495175
Mean1.0095774
Median Absolute Deviation (MAD)0.0073
Skewness-1.1016696
Sum236.2411
Variance0.00017915781
MonotonicityNot monotonic
2025-04-20T16:03:15.889187image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0038 4
 
1.7%
1.0052 3
 
1.3%
1.0058 3
 
1.3%
1.0079 3
 
1.3%
1.0074 3
 
1.3%
1.0015 3
 
1.3%
1.0108 3
 
1.3%
1.0123 2
 
0.9%
1.0184 2
 
0.9%
1.0043 2
 
0.9%
Other values (170) 206
88.0%
ValueCountFrequency (%)
0.912 1
0.4%
0.9816 1
0.4%
0.9831 1
0.4%
0.9849 1
0.4%
0.9869 1
0.4%
0.9876 1
0.4%
0.9886 2
0.9%
0.9897 1
0.4%
0.9927 1
0.4%
0.9937 1
0.4%
ValueCountFrequency (%)
1.0691 1
0.4%
1.0404 1
0.4%
1.0378 1
0.4%
1.0376 1
0.4%
1.0359 1
0.4%
1.0325 1
0.4%
1.0319 1
0.4%
1.0316 1
0.4%
1.0315 1
0.4%
1.0314 1
0.4%

World Population Percentage
Real number (ℝ)

High correlation  Zeros 

Distinct70
Distinct (%)29.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.42705128
Minimum0
Maximum17.88
Zeros57
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-04-20T16:03:16.090003image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01
median0.07
Q30.28
95-th percentile1.485
Maximum17.88
Range17.88
Interquartile range (IQR)0.27

Descriptive statistics

Standard deviation1.7149768
Coefficient of variation (CV)4.0158568
Kurtosis90.464602
Mean0.42705128
Median Absolute Deviation (MAD)0.07
Skewness9.1511037
Sum99.93
Variance2.9411453
MonotonicityNot monotonic
2025-04-20T16:03:16.279253image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 57
24.4%
0.01 18
 
7.7%
0.03 11
 
4.7%
0.07 11
 
4.7%
0.02 9
 
3.8%
0.13 7
 
3.0%
0.09 7
 
3.0%
0.04 7
 
3.0%
0.11 5
 
2.1%
0.06 5
 
2.1%
Other values (60) 97
41.5%
ValueCountFrequency (%)
0 57
24.4%
0.01 18
 
7.7%
0.02 9
 
3.8%
0.03 11
 
4.7%
0.04 7
 
3.0%
0.05 4
 
1.7%
0.06 5
 
2.1%
0.07 11
 
4.7%
0.08 3
 
1.3%
0.09 7
 
3.0%
ValueCountFrequency (%)
17.88 1
0.4%
17.77 1
0.4%
4.24 1
0.4%
3.45 1
0.4%
2.96 1
0.4%
2.74 1
0.4%
2.7 1
0.4%
2.15 1
0.4%
1.81 1
0.4%
1.6 1
0.4%

Interactions

2025-04-20T16:03:06.508748image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:47.821769image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:49.258742image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:50.795309image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:52.350326image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:53.925256image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:55.487934image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:57.191559image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:58.723657image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:00.207581image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:01.701142image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:03.279972image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:04.946084image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:06.618227image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:47.939752image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:49.366446image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:50.902889image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:52.456788image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:54.032912image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:55.784625image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:57.294949image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:58.826431image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:00.338638image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:01.808823image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:03.380400image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:05.053560image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:06.743769image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:48.052384image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:49.490190image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:51.025419image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:52.588528image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:54.155642image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:55.901669image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:57.415421image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:58.944062image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:00.455268image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:01.935760image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:03.496153image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:05.180161image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:06.869546image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:48.166177image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:49.611846image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:51.150170image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:52.710277image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:54.278441image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:56.024734image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:57.536178image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:59.061889image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:00.572831image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:02.065654image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:03.616811image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:05.309347image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:06.992285image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:48.277980image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:49.732688image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:51.273761image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:52.834257image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:54.402278image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:56.144481image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:57.655993image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:59.177766image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:00.688484image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:02.194506image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:03.735611image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:05.434943image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:07.122123image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:48.393529image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:49.853607image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:51.400623image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:52.956013image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:54.532167image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:56.265291image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:57.778647image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:59.294365image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:00.804992image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:02.320227image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:03.854378image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:05.557781image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:07.247748image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:48.502161image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:49.971439image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:51.517490image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:53.075806image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:54.651970image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:56.379972image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:57.896170image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:59.408347image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:00.915641image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:02.440954image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:03.967164image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:05.678536image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:07.368442image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:48.611764image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:50.090255image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:51.639276image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:53.195276image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:54.768721image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:56.495942image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:58.013868image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:59.522127image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:01.030299image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:02.561541image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:04.080777image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:05.797074image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:07.487928image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:48.716439image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:50.207933image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:51.754674image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:53.310025image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:54.884490image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:56.605571image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:58.126368image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:59.630792image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:01.138972image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:02.677334image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:04.191247image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:05.914877image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:07.597347image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:48.815104image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:50.315595image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:51.864276image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:53.425688image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:54.996286image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:56.713258image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:58.235803image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:59.744410image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:01.239431image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:02.788015image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:04.292728image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:06.026425image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:07.725856image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:48.932801image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:50.440195image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:51.991122image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:53.553285image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:55.122021image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:56.835018image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:58.363525image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:59.863024image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:01.364177image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:02.910746image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:04.412405image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:06.152913image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:07.838338image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:49.030441image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:50.547869image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:52.100700image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:53.661993image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:55.229595image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:56.947412image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:58.474282image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:59.967684image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:01.462870image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:03.017439image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:04.709626image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:06.261473image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:07.968103image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:49.145101image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:50.672451image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:52.223449image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:53.791617image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:55.365391image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:57.069930image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:02:58.601038image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:00.086698image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:01.583430image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:03.156346image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:04.826532image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T16:03:06.385224image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-04-20T16:03:16.425683image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
1970 Population1980 Population1990 Population2000 Population2010 Population2015 Population2020 Population2022 PopulationArea (km²)ContinentDensity (per km²)GrowthRateRankWorld Population Percentage
1970 Population1.0000.9980.9940.9870.9770.9720.9680.9660.8060.000-0.1320.137-0.9660.958
1980 Population0.9981.0000.9980.9930.9850.9810.9770.9760.8160.000-0.1350.159-0.9760.968
1990 Population0.9940.9981.0000.9980.9920.9890.9860.9850.8260.014-0.1380.192-0.9850.977
2000 Population0.9870.9930.9981.0000.9970.9950.9940.9930.8360.054-0.1420.225-0.9930.985
2010 Population0.9770.9850.9920.9971.0000.9990.9980.9980.8430.063-0.1450.255-0.9980.990
2015 Population0.9720.9810.9890.9950.9991.0001.0000.9990.8450.063-0.1460.271-0.9990.992
2020 Population0.9680.9770.9860.9940.9981.0001.0001.0000.8480.063-0.1490.287-1.0000.992
2022 Population0.9660.9760.9850.9930.9980.9991.0001.0000.8490.063-0.1510.294-1.0000.992
Area (km²)0.8060.8160.8260.8360.8430.8450.8480.8491.0000.088-0.5840.356-0.8490.841
Continent0.0000.0000.0140.0540.0630.0630.0630.0630.0881.0000.0000.2940.2630.063
Density (per km²)-0.132-0.135-0.138-0.142-0.145-0.146-0.149-0.151-0.5840.0001.000-0.2220.151-0.154
GrowthRate0.1370.1590.1920.2250.2550.2710.2870.2940.3560.294-0.2221.000-0.2940.278
Rank-0.966-0.976-0.985-0.993-0.998-0.999-1.000-1.000-0.8490.2630.151-0.2941.000-0.992
World Population Percentage0.9580.9680.9770.9850.9900.9920.9920.9920.8410.063-0.1540.278-0.9921.000

Missing values

2025-04-20T16:03:08.155112image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-20T16:03:08.469169image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

RankCCA3Country/TerritoryCapitalContinent2022 Population2020 Population2015 Population2010 Population2000 Population1990 Population1980 Population1970 PopulationArea (km²)Density (per km²)GrowthRateWorld Population Percentage
036AFGAfghanistanKabulAsia411287713897223033753499281896721954298210694796124866311075297165223063.05871.02570.52
1138ALBAlbaniaTiranaEurope284232128668492882481291339931820213295066294165123247312874898.87020.99570.04
234DZAAlgeriaAlgiersAfrica4490322543451666395431543585634430774621255180741873937813795915238174118.85311.01640.56
3213ASMAmerican SamoaPago PagoOceania4427346189513685484958230478183288627075199222.47740.98310.00
4203ANDAndorraAndorra la VellaEurope7982477700717467151966097535693561119860468170.56411.01000.00
542AGOAngolaLuandaAfrica35588987334284852812772123364185163940621182863883300476029700124670028.54661.03150.45
6224AIAAnguillaThe ValleyNorth America158571558514525131721104783166560628391174.25271.00660.00
7201ATGAntigua and BarbudaSaint John’sNorth America9376392664899418569575055633286488864516442212.13351.00580.00
833ARGArgentinaBuenos AiresSouth America4551031845036032432570654110012337070774326376572802480323842803278040016.36831.00520.57
9140ARMArmeniaYerevanAsia278046928056082878595294629331685233556539313512325343772974393.48310.99620.03
RankCCA3Country/TerritoryCapitalContinent2022 Population2020 Population2015 Population2010 Population2000 Population1990 Population1980 Population1970 PopulationArea (km²)Density (per km²)GrowthRateWorld Population Percentage
22443UZBUzbekistanTashkentAsia346276523352665630949417286142272492555420579100159471291201136144740077.39751.01600.43
225181VUTVanuatuPort-VilaOceania326740311685276438245453192074150882118156870191218926.80611.02380.00
226234VATVatican CityVatican CityEurope5105205645966517007337521510.00000.99800.00
22751VENVenezuelaCaracasSouth America283016962849045330529716287150222442772919750579152104431135547591644530.88201.00360.35
22816VNMVietnamHanoiAsia9818685696648685921913988741101279001142669126135296827041928849331212296.44721.00741.23
229226WLFWallis and FutunaMata-UtuOceania11572116551218213142147231345411315937714281.49300.99530.00
230172ESHWestern SaharaEl AaiúnAfrica575986556048491824413296270375178529116775763712660002.16541.01840.01
23146YEMYemenSanaaAsia3369661432284046285165452474394618628700133751219204938684360752796863.82321.02170.42
23263ZMBZambiaLusakaAfrica20017675189277151624823013792086989113676864015720438428167175261226.59761.02800.25
23374ZWEZimbabweHarareAfrica1632053715669666141549371283977111834676101138937049926520291839075741.76651.02040.20